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, and simple: key goals for future 6G networks and the Internet of Things. Using mathematical modeling, computer simulations, and practical experiments with software-defined radios, the research will
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estimation algorithms that operate reliably in challenging environments (e.g., low texture, illumination variation, dynamic scenes, or sensor degradation). The research will integrate model-based estimation
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-boundary value problem is reformulated in such a way that loads, boundary conditions and constitutive models are completely or partially replaced by some form of experimental data. The research activities
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recently proposed foundation models. The research fellow will conduct a systematic review of the state of the art, identifying the main methodologies, models, and tools currently available for automatic
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Position Description Role type: Full Time, Fixed Term for 2 years Faculty: Faculty of Science School: School of Mathematics and Statistics Salary: Academic Level A.6 - $110,319 p.a. plus 17% super
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machine learning methods, including cluster analysis and predictive modeling, to identify distinct phenotypes of diabetes and characterize factors associated with disease onset, progression, complications
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of waves, and the turbulent mixing and entrainment of air bubbles and oil droplets, with the aim of improving existing modelling tools for marine oil spills. The Post.Doc. will be a key person in the project
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, DigiSilent PowerFactory, Siemens PSS-E * Hardware-in-the-loop (HIL) & Software-in-the-loop Platforms: OPAL-RT/RTDS solutions. Mathematical modelling of the system-level and device-level constraints
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 3 months ago
appointment for a period of two years (24 months) to contribute to research in artificial intelligence, with a particular focus on large language models and their applications. Applications are particularly
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, COMPASS-31 measures, etc. The project may include descriptive analysis, multivariable modeling, and integration of findings across epidemiologic datasets to improve understanding of infection-associated